7 papers · 1 filter
UniNDM: A Unified Noise-driven Detection and Mitigation Framework Against Sexual Content in Text-to-Image Generation
Yao Huang, Yitong Sun, Huanran Chen +8
Despite the impressive generative capabilities of text-to-image diffusion models, they remain vulnerable to implicit sexual prompts, where subtle cues disguised as benign terms or…
Adversarial Orthogonal Disentanglement for LVLM Hallucination Mitigation
Ruoxi Cheng, Haoxuan Ma, Zhengfei Hai +6
Large Vision-Language Models (LVLMs) have advanced multimodal understanding, yet their reliability is limited by hallucination, where generated content conflicts with visual facts.…
Knowledge-Guided Adversarial Training for Infrared Object Detection via Thermal Radiation Modeling
Shiji Zhao, Shukun Xiong, Maoxun Yuan +6
In complex environments, infrared object detection exhibits broad applicability and stability across diverse scenarios. However, infrared object detection is vulnerable to both com…
MoAPT: Mixture of Adversarial Prompt Tuning for Vision-Language Models
Shiji Zhao, Qihui Zhu, Shukun Xiong +7
Large pre-trained Vision Language Models (VLMs) demonstrate excellent generalization capabilities but remain highly susceptible to adversarial examples, posing potential security r…
VRSA: Jailbreaking Multimodal Large Language Models through Visual Reasoning Sequential Attack
Shiji Zhao, Shukun Xiong, Yao Huang +7
Multimodal Large Language Models (MLLMs) are widely used in various fields due to their powerful cross-modal comprehension and generation capabilities. However, more modalities bri…
NDM: A Noise-driven Detection and Mitigation Framework against Implicit Sexual Intentions in Text-to-Image Generation
Yitong Sun, Yao Huang, Ruochen Zhang +4
Despite the impressive generative capabilities of text-to-image (T2I) diffusion models, they remain vulnerable to generating inappropriate content, especially when confronted with…